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The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment was carried out in the environs of Manaus, Brazil, in the central region of the Amazon basin during two years from 1 January 2014 through 31 December 2015. The experiment focused on the complex interactions among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other. The objective was to understand and quantify these linked processes, first under natural conditions to obtain a baseline and second when altered by the effects of human activities. To this end, the pollution plume from the Manaus metropolis, superimposed on the background conditions of the central Amazon basin, served as a natural laboratory. The present paper, as the Introduction to the GoAmazon2014/5 Special Issue, presents the context and motivation of the GoAmazon2014/5 Experiment. The nine research sites, including the characteristics and instrumentation of each site, are presented. The sites range from time point zero (T0) upwind of the pollution, to T1 in the midst of the pollution, to T2 just downwind of the pollution, to T3 furthest downwind of the pollution (70 km). In addition to the ground sites, a low-altitude G-159 Gulfstream I (G1) observed the atmospheric boundary layer and low clouds, and a high-altitude Gulfstream G550 (HALO) operated in the free troposphere. During the two-year experiment, two Intensive Operating Periods (IOP1 and IOP2) also took place that included additional specialized research instrumentation at the ground sites as well as flights of the two aircraft. GoAmazon2014/5 IOP1 was carried out from 1 February to 31 March 2014 in the wet season. GoAmazon2014/5 IOP2 was conducted from 15 August to 15 October 2014 in the dry season. The G1 aircraft flew during both IOP1 and IOP2, and the HALO aircraft flew during IOP2. In the context of the Amazon basin, the two IOPs also correspond to the clean and biomass burning seasons, respectively. The Manaus plume is present year round, and it is transported by prevailing northeasterly and easterly winds in the wet and dry seasons, respectively. This Introduction also organizes information relevant to many papers in the Special Is-sue. Information is provided on the vehicle fleet, power plants, and industrial activities of Manaus. The mesoscale and synoptic meteorologies relevant to the two IOPs are presented. Regional and long-range transport of emissions during the two IOPs is discussed based on satellite observations across South America and Africa. Fire locations throughout the airshed are detailed. In conjunction with the context and motivation of GoAmazon2014/5, as presented herein in this Introduction, research articles published in this Special Issue are anticipated in the near future to describe the detailed results and findings of the GoAmazon2014/5 Experiment. [ABSTRACT FROM AUTHOR]

Understanding the aerosol influence on clouds and precipitation is an important key to reduce uncertainties in simulations of climate change scenarios with regards to deforestation fires. Here, we associate rainfall characteristics obtained by an S-Band radar in the Amazon with in situ measurements of biomass burning aerosols for the entire year of 2009. The most important results were obtained during the dry semester (July-December). The results indicate that the aerosol influence on precipitating systems is modulated by the atmospheric instability degree. For stable atmospheres, the higher the aerosol concentration, the lower the precipitation over the region. On the other hand, for unstable cases, higher concentrations of particulate material are associated with more precipitation, elevated presence of ice and larger rain cells, which suggests an association with long lived systems. The results presented were statistically significant. However, due to the limitation imposed by the dataset used, some important features such as wet scavenging and droplet size distribution need further clarification. Regional climate model simulations in addition with new field campaigns could aggregate information to the aerosol/precipitation relationship. [ABSTRACT FROM AUTHOR]

The article presents a study which uses a polarimetric X-band radar in an effort to derive observations of tropical rain. The study describes the polarimetric X-band radar's specifications and discusses the calculation of polarimetric moments from disdrometer measurements. It also presents results from the data processing with the methods of Kalman filter (EKF) and ZPHI.

This work presents results which help to understand the behaviour of the Normalized Difference Vegetation Index (NDVI) anomalies over the South America continent during the two strongest El Niño events of the last century (1982-1983 and 1997-1998). The data used are parts of a long-term series (July 1981 to December 1999) of calibrated NDVI data derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) datasets. Special emphasis has been given to the analysis of the response of the major Brazilian vegetation types. This paper introduces an approach that enhances NDVI anomalies relative to the long-term climatology of the region. We find a negative NDVI anomaly for most of the region during the 1982-1983 event, whereas for the 1997-1998 event positive NDVI anomalies were observed over most regions. Only the 'Nordeste' region showed a similar vegetation response for both events. We identify three possible factors that may play a role in the different NDVI responses to the two El Niño events. Firstly, poor intercalibration of sensors may account for some, but not all of the differences. Secondly the response of the vegetation may depend upon the climate conditions prior to the El Niño events. Thirdly, the difference in the onset date and the duration of the mature phase of the two El Niño events, associated with very different Atlantic surface temperatures are shown to have dynamical consequences which may impact upon the vegetation. [ABSTRACT FROM AUTHOR]